Engineering Commons^™

Preparing Homogenous Composites Of Collagen And Cellulose Nanocrystals For Tissue Engineering Research, Zachary Stanley

Biological and Agricultural Engineering Undergraduate Honors Theses

Advancements in medicine and our understanding of stem cells have led to a greater emphasis on further developing research focused on tissue engineering. This research has led to the rise of both two-dimensional and three-dimensional scaffolds that can be utilized to repair bone, skin, vascular, and potentially even nervous tissue. One of the prominent compounds used in modern scaffolds is collagen-based hydrogels due to their low antigenicity and ability to provide structure to cells. There is potential to further improve upon this three-dimensional scaffold by incorporating cellulose nanocrystals (CNCs) into a composite hydrogel with collagen. The addition would increase the …

Analyzing Energy Use From Arkansas Public School Buildings, Oscar Morton

Biological and Agricultural Engineering Undergraduate Honors Theses

Energy is a key global resource in society and greenhouse gas (GHG) emissions related to the consumption of energy have detrimental effects on the planet. With energy consumption increasing, exponential population growth, and diminishing natural resources necessary for energy production, the relevance of energy efficiency is dramatically increasing. In this project, I analyzed energy usage data from a set of public school buildings from one school district in northwest Arkansas from 2018, before targeted energy efficiency practices were implemented, through 2022 to see if energy usage changed over this short period. The objectives of analyzing energy data were to determine …

Development Of Medical Device For Volume Status Assessment Using Peripheral Venous Pressure, Audra Beneux

Graduate Theses and Dissertations

The severe side effects of acute dehydration and blood loss may be prevented if assessed and treated quickly. As amplifier technology has improved, small peripheral venous pressure (PVP) signals collected using a transducer on an intravenous catheter can be analyzed to monitor patient volume status. This thesis details the development of an embedded system prototype that quickly and accurately assesses volume status using PVP analysis. The volume prediction algorithm classified 10 seconds of PVP data as “dehydrated” or “resuscitated”. The hardware design consisted of four primary areas: analog signal conditioning, processing, user interaction, and power. After data collection, the signal …

Digital Simulations Of Memristors Towards Integration With Reconfigurable Computing, Ivris Raymond

Computer Science and Computer Engineering Undergraduate Honors Theses

The end of Moore’s Law has been predicted for decades. Demand for increased parallel computational performance has been increased by improvements in machine learning. This past decade has demonstrated the ever-increasing creativity and effort necessary to extract scaling improvements in CMOS fabrication processes. However, CMOS scaling is nearing its fundamental physical limits. A viable path for increasing performance is to break the von Neumann bottleneck. In-memory computing using emerging memory technologies (e.g. ReRam, STT, MRAM) offers a potential path beyond the end of Moore’s Law. However, there is currently very little support from industry tools for designers wishing to incorporate …

A Novel Deep Learning, Camera, And Sensorbased System For Enforcing Hand Hygiene Compliance In Healthcare Facilities, Samyak Shrimali, Christof Teuscher

Electrical and Computer Engineering Faculty Publications and Presentations

Hospital-acquired infections are a major cause of death worldwide, and poor hand hygiene compliance is a primary reason for their spread. This paper proposes an artificial intelligence, microcontroller, and sensor-based system that monitors and improves staff hand hygiene compliance at various critical points in a hospital. The system uses a Convolutional Neural Network (CNN) to detect and track if staff have followed the WHO hand rub/hand wash guidelines at alcohol dispensers, hospital sinks, and patient beds. The system also uses RFID tags, vibration motors, LEDs, and a central server to identify staff, alert them of their cleaning requirements, monitor their …

The Networked Nitrous Node: A Low-Power Field-Deployable Cots-Based N2o Gas Sensor Platform, Ronaldo Leon, Wenyu Bi, Eyal Eynis, Travis Johnson, Wei Yan, David C. Burnett, John M. Acken

Electrical and Computer Engineering Faculty Publications and Presentations

We present a wireless nitrous oxide (N 2 O) gas sensor system consisting of a commercial high-current infrared N 2 O sensor wrapped in a “smart” sensor framework to make it suitable for battery-powered deployment. This framework consists of wireless mesh networking, data storage, additional environmental sensors, and a gas sensor power control circuit managed by a central microcontroller. The N 2 O sensor is the first order consumer of power and sampling N 2 O at approximately ten minute intervals yields an estimated system lifetime of 63 days when using four 18650 Li-ion batteries. The node stores data locally …

Studying Pedestrian’S Unmarked Midblock Crossing Behavior On A Multilane Road When Interacting With Autonomous Vehicles Using Virtual Reality, Fengjiao Zou

All Dissertations

This dissertation focuses on the challenge of pedestrian interaction with autonomous vehicles (AVs) at unmarked midblock locations where the right-of-way is unspecified. A virtual reality (VR) simulation was developed to replicate an urban unmarked midblock environment where pedestrians cross a four-lane arterial roadway and interact with AVs. One research goal is to investigate the impact of roadway centerline features (undivided, two-way left-turn lane, and median) and AV operational schemes portrayed through on-vehicle signals (no signal, yellow negotiating indication, and yellow/blue negotiating/no-yield indications) on pedestrian crossing behavior. Results demonstrate that both roadway centerline design features and AV operations and signaling show …

Explainable Physics-Informed Deep Learning For Rainfall-Runoff Modeling And Uncertainty Assessment Across The Continental United States, Sadegh Sadeghi Tabas

All Dissertations

Hydrologic models provide a comprehensive tool to calibrate streamflow response to environmental variables. Various hydrologic modeling approaches, ranging from physically based to conceptual to entirely data-driven models, have been widely used for hydrologic simulation. During the recent years, however, Deep Learning (DL), a new generation of Machine Learning (ML), has transformed hydrologic simulation research to a new direction. DL methods have recently proposed for rainfall-runoff modeling that complement both distributed and conceptual hydrologic models, particularly in a catchment where data to support a process-based model is scared and limited.

This dissertation investigated the applicability of two advanced probabilistic physics-informed DL …

Assessing What We Value: Interactions Between Student Perceptions Of Assessments In The Calculus Classroom And Their Future-Oriented Motivation, Catherine Kenyon

All Dissertations

This work investigates interactions between first-year engineering (FYE) student perceptions of Calculus exams, their perceptions of the future, and their levels of math test anxiety. All phases of this study were conducted at a very high research (R1) institution with a common FYE program and coordinated Calculus courses. An initial pilot study explored how FYE students perceive the purpose of taking Calculus exams and how math test anxiety may play a role in these students’ perceptions of exams and their future in engineering. A second pilot study expanded on these results concerning student perceptions of Calculus exams and math test …

Accelerating The Derivation Of Optimal Powertrain Control Strategies Using Reinforcement Learning And Virtual Prototypes, Daniel Egan

All Dissertations

The push for improvements in fuel economy while reducing tailpipe emissions has resulted in significant increases in automotive powertrain complexity, subsequently increasing the resources, both time and money, needed to develop them. Powertrain performance is heavily influenced by the quality of their controller/calibration with modern powertrains reaching levels of complexity where using traditional design of experiment-based methodologies to develop them can take years. Recently, reinforcement learning (RL), a machine learning technique, has emerged as a method to rapidly create optimal controllers for systems of unlimited complexity directly which creates an opportunity to use RL to reduce the overall time and …

Beyond Just Money Transactions: Redesigning Digital Peer-To-Peer Payments For Social Connections, Lingyuan Li

All Dissertations

Financial activities, such as the exchange of money between individuals, have long been considered a crucial aspect of how people build and maintain their interpersonal relationships (i.e., a strong, deep, or close association/acquaintance between two or more people) with individuals they know because money is a sensitive social construct. In particular, over the past decade, how to conduct, manage, and experience money exchanges and processes between individuals has been dramatically transformed due to the increasing popularity of digital peer-to-peer (P2P) payment services (i.e., performing one to one online money transactions via a digital device). In this sense, digital P2P payments …

Classification Of Electrical Current Used In Electroplastic Forming, Tyler Grimm

All Dissertations

Electrically assisted manufacturing (EAM) is the direct application of an electric current to a workpiece during manufacturing. This advanced manufacturing process has been shown to produce anomalous effects which extend beyond the current state of modeling of thermal influences. These purported non-thermal effects have collectively been termed electroplastic effects (EPEs).

While there is a distinct difference in results between steady-state (ideal DC) testing and pulsed current testing, the very definition of these two EAM methods has not been well established. A "long" pulse may be considered DC current; a "short" pulse may produce electroplastic effects; and even "steady-state" current shapes …

Deep Reinforcement Learning And Game Theoretic Monte Carlo Decision Process For Safe And Efficient Lane Change Maneuver And Speed Management, Shahab Karimi

All Dissertations

Predicting the states of the surrounding traffic is one of the major problems in automated driving. Maneuvers such as lane change, merge, and exit management could pose challenges in the absence of intervehicular communication and can benefit from driver behavior prediction. Predicting the motion of surrounding vehicles and trajectory planning need to be computationally efficient for real-time implementation. This dissertation presents a decision process model for real-time automated lane change and speed management in highway and urban traffic. In lane change and merge maneuvers, it is important to know how neighboring vehicles will act in the imminent future. Human driver …

Elucidating The Mechanical And Transport Properties Of Lignin-Based Hydrogel Composites, Nicholas Gregorich

All Dissertations

The use of lignin in the fabrication of soft composites has become an emerging area of research in polymer science and polymer chemistry. These lignin-based materials present numerous benefits, notably, a reduction in the use of petroleum-based precursor, improved structural benefits to otherwise soft host polymers, as well as the inherent antimicrobial and antioxidant properties of lignin, making it suitable for biomaterials. Herein, we present two chemical reaction pathways of incorporating lignin that was fractionated and cleaned using the Aqueous Lignin Purification with Hot Agents (ALPHA) process into poly(vinyl alcohol) (PVA) hydrogel composites for aqueous-based separations. By leveraging the ALPHA …

Design And Implementation Of High-Efficiency, Lightweight, System-Friendly Solid-State Circuit Breaker, Dehao Qin

All Dissertations

Direct current (DC) distribution system has shown potential over the alternative current (AC) distribution system in some application scenarios, e.g., electrified transportation, renewable energy, data center, etc. Because of the fast response speed, DC solid-state circuit breaker (SSCB) becomes a promising technology for the future power electronics intensive DC energy system with fault-tolerant capability. First, a thorough literature survey is performed to review the DC-SSCB technology. The key components for DC-SSCB, including power semiconductors, topologies, energy absorption units, and fault detection circuits, are studied. It is observed that the prior studies mainly focus on the basic interruption capability of the …

The Effects Of Laser Shock Peening On The Fatigue Life Of Additive Manufactured Alsi10mg, Jacob L. Biddlecom

All Dissertations

Additive manufacturing (AM) is becoming a manufacturing process that is well established, even with all the resources and attention that has been brought to it, the field is still lacking some key understandings. Currently, there are certain aspects that are difficult to overcome. Some of the intrinsic obstacles include process-induced defects, such as porosity from lack of fusion and gaseous bubble entrapment, as well as complex thermal gradients. These defects can lead to altered material response especially when looking at the fatigue life. The fatigue behaviors of AM components can change from print to print as well as when compared …

Vanet Applications Under Loss Scenarios & Evolving Wireless Technology, Adil Alsuhaim

All Dissertations

In this work we study the impact of wireless network impairment on the performance of VANET applications such as Cooperative Adaptive Cruise Control (CACC), and other VANET applications that periodically broadcast messages. We also study the future of VANET application in light of the evolution of radio access technologies (RAT) that are used to exchange messages. Previous work in the literature proposed fallback strategies that utilizes on-board sensors to recover in case of wireless network impairment, those methods assume a fixed time headway value, and do not achieve string stability. In this work, we study the string stability of a …

Enhanced Mobile Networking Using Multi-Connectivity And Packet Duplication In Next-Generation Cellular Networks, Prabodh Mishra

All Dissertations

Modern cellular communication systems need to handle an enormous number of users and large amounts of data, including both users as well as system-oriented data. 5G is the fifth-generation mobile network and a new global wireless standard that follows 4G/LTE networks. The uptake of 5G is expected to be faster than any previous cellular generation, with high expectations of its future impact on the global economy. The next-generation 5G networks are designed to be flexible enough to adapt to modern use cases and be highly modular such that operators would have the flexibility to provide selective features based on user …

A Study On Asymmetric Perfect Vortex: Fractional Orbital Angular Momentum And Nonlinear Interaction, Kunjian Dai

All Dissertations

In this work, the manipulation including generation and detection of the asymmetric perfect vortex (APV) carrying fractional orbital angular momentum (OAM) was demonstrated and discussed. All the manipulation of the modes is in real-time which provides a perfect tool for sensing the dynamic properties of complex media. The OAM-involved nonlinear conversion, specifically the second-harmonic generation (SHG) using the APV and asymmetric Bessel-Gaussian (BG) beams was studied in detail.

The generation and detection of the APV are based on the HOBBIT concept which includes acoustic optical deflector (AOD) and log-polar coordinate transformation optics. The RF signal driving the AOD allows the …

Bridging The Gap Between Phase Behavior And Processing For Renewable And Refinable Carbonaceous Precursors, Graham Tindall

All Dissertations

As a category of materials, engineered carbons, specifically carbon fibers, are first-in-class for properties such as modulus, specific strength, and thermal resilience; however, the inability to directly process atomic carbon necessitates the development and optimization of carbonaceous precursors. Because the structure and properties of carbon are highly dependent on the precursors and requisite processing, numerous materials have been investigated as feedstocks for large-scale production. Although cellulose and rayon were among the first investigated, polyacrylonitrile (PAN) is the current hegemon of carbon fiber precursors. PAN feeds 90% of this market, but it is neither inexpensive nor renewable. Because a significant fraction …

Fluorescence Multiplexing With Combination Probes For Biological And Diagnostic Applications, Madeline Mccarthy

All Dissertations

Cancer refers to a group of diseases containing more than 200 different subtypes. Cancer is a heterogeneous disease by nature, meaning that there are differences among tumors of the same type in different patients, and there are differences among cancer cells within a single tumor of one patient. Since cancer is not a single disease, nor does it have a single cause, it proves to be incredibly hard to diagnose and treat. The ability to study cellular markers, cell and tissue spatial arrangement, and gene function are all integral parts of cancer diagnostic and treatment efforts.

Here, I first present …

Computational Modeling Of Patterned Membranes And Spacers For Improved Hydrodynamics And Fouling Reduction In Reverse Osmosis Water Treatment Processes, Zuo Zhou

All Dissertations

My research goal is to discover ways to improve the hydrodynamics of reverse osmosis (RO) membrane systems through creative membrane surface patterning and spacer designs. Since concentration polarization (CP) usually promotes membrane fouling, improving hydrodynamics would result in reduced fouling and better membrane performance. With computational fluid dynamics (CFD), we can explore dozens or even hundreds of models with different geometries and boundary conditions. Through plotting their velocity profile, streamlines, shear stress, pressure profile, concentration profile, and so on, we can determine which design would lead to the best performance.

At first, patterned membranes were evaluated and compared with flat …

Molecular Dynamics Simulation On Molybdenum Disulfide: Thermal-Mechanical Properties And Phase Transitions Under External Loading, Mahabubur Rahman

All Dissertations

Due to their remarkable properties, transition metal dichalcogenides (TMDs) have received much scientific interest throughout the past decade. Two layers of chalcogen atoms (S, Se, Te) sandwich a layer of transition metal atoms (Mo, W, Ta) to form the three-atom thick unit cell in TMDs. The interaction between TMD "single layers" is mediated by neighboring chalcogen planes and bonded by Van der Waals forces. Due to this weak out-of-plane interaction, bulk samples can be thinned down to a single layer by exfoliation. Among the TMDs, Molybdenum Disulfide (MoS₂) shows promise in the field of electronics, optics, and sensing …

3d Printing With Photopolymerizable Polyester Resins For Resorbable Medical Device Applications, Mathew Murphy Stanford

All Dissertations

In the past decade, the healthcare industry has seen a significant increase in the use of additive manufacturing (AM or “3D printing”) with subsequent improvement in clinical outcomes.As an exceptional AM technology, vat-photopolymerization (VP), often called stereolithography, can create complex structures and has thus been adopted for a range of biomedical applications including surgical guides, temporary implants, and resorbable tissue scaffolds.However, limitations remain in the availability of photopolymerizable resin materials with appropriate mechanical performance, biodegradability, and biocompatibility for application to resorbable medical devices.

The objective of this work was to employ novel photopolymerizable polyester-based macromers in the development of resorbable …

Mesoscale Modeling Of Controlled Degradation In Polymer Networks And Melts, Vaibhav Palkar

All Dissertations

Controlled degradation of polymers finds various applications in fields ranging from the design of functional soft materials to recycling of polymers. In several of these applications, the characteristic length scale at which relevant processes occur ranges from nanometers to microns, typically referred to as the mesoscale. Although analytical models and continuum approaches inform our current understanding, analysis of degradation at the mesoscale is exceptionally limited. For modeling degradation at the mesoscale, we use the Dissipative Particle Dynamics (DPD) technique and the LAMMPS simulation software. Within the DPD framework, we model controlled degradation or the breaking of covalent bonds within a …

Explaining Spatio-Temporal Evolution Of Extreme Hydro-Climatic Events Using A Complex Network Framework, Somnath Mondal

All Dissertations

Severe hydroclimatic extreme events, such as droughts, heatwaves, and heavy rainfall, are occurring with increasing frequency and causing significant impacts on both people and the environment. These events also compound in space and time, leading to even more significant consequences. Therefore, it is essential to comprehend these phenomena' concurrent and time-delayed progression across different temporal and spatial scales to address adaptation and mitigation effectively. To accurately understand and map the co-evolution of extreme events, it's necessary to have a thorough grasp of their spatiotemporal patterns, how they propagate and interact with one another, and the underlying mechanisms driving their occurrence. …

A Pwm Method For Reducing Dv/Dt And Switching Losses In Two-Stage Power Converters, Mahima Gupta, Abhijeet Prem

Electrical and Computer Engineering Faculty Publications and Presentations

Today's semiconductor devices are accompanied by high switching frequencies (> kilo-hertz) and small transition times (< micro-seconds). Such fast transition times are accompanied by undesirable effects such as voltage overshoots at the load terminals, ground leakage currents, wide-band electromagnetic noise, etc. With the advent of wide band-gap devices, several applications are moving towards higher switching frequency operation with at-least an order of magnitude reduction in transition times. While these characteristics are considered necessary to break the next-generation barriers in power density, efficiency and applicability, the undesirable effects due to faster transitions are expected to present obstacles. This work proposes a PWM approach to modify the shape of the switching voltages to overcome the disadvantages of the fast transition times without any increase in switching losses. In fact, several of the switching transitions feature ZVS operation, resulting in reduced switching losses. The paper discusses the analytical details of the approach using a simple DC-DC boost-buck converter and extends it to a DC to three-phase AC converter using the principles of space vector modulation. The paper presents detailed simulation and comparative results in terms of voltage over-shoots over long cables, loss calculations and electromagnetic noise. Results from a laboratory-scale working prototype confirm the benefits of the proposed approach in terms of EMI and loss reduction.

When Less Is More: How Increasing The Complexity Of Machine Learning Strategies For Geothermal Energy Assessments May Not Lead Toward Better Estimates, Stanley P. Mordensky, John Lipor, Jacob Deangelo, Erick R. Burns, Cary R. Lindsey

Electrical and Computer Engineering Faculty Publications and Presentations

Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of error that reduces the predictive performance of the models and confidence in the resulting resource estimates.

Our study aims to develop robust data-driven methods with the goals of reducing bias and improving predictive ability. We present and compare nine favorability maps for geothermal resources in the …

Near-Optimal Control Of A Quadcopter Using Reinforcement Learning, Alberto Velazquez-Estrada

Theses and Dissertations

This paper presents a novel control method for quadcopters that achieves near-optimal tracking control for input-affine nonlinear quadcopter dynamics. The method uses a reinforcement learning algorithm called Single Network Adaptive Critics (SNAC), which approximates a solution to the discrete-time Hamilton-Jacobi-Bellman (DT-HJB) equation using a single neural network trained offline. The control method involves two SNAC controllers, with the outer loop controlling the linear position and velocities (position control) and the inner loop controlling the angular position and velocities (attitude control). The resulting quadcopter controller provides optimal feedback control and tracks a trajectory for an infinite-horizon, and it is compared with …

Trajectory Analysis For Driving Safety Quantification, Michael I. Chang

UNLV Theses, Dissertations, Professional Papers, and Capstones

In order to evaluate the efficacy of the skid recovery exercise in the Driver’s Edge teenage driving program, a process is established to determine the trajectories of vehicles from recorded videos, compare them in terms of similarity through dynamic time warping (DTW), and then analyze the similarity measurements to assess whether the program has a significant effect on driving ability by repeated measures analysis of variance (rANOVA). The video is analyzed by Harris corner detection and Lucas-Kanade optical flow method to ascertain the vehicle trajectories. A homography is then estimated to translate coordinates from video into real-world. The instructor and …